probPicked=1-cdf('norm',46.7,43.1,3.6)% this is the probability that you 
%will pick a value of 46.7 or greater from a normal distribution with a
%mean of 43.1 and a std of 3.6 once.  The once is key to understanding the
%rest of this.  Once means that if you litteraly used a random number
%generator to draw a value from the above normal distribution one time,
%this is the probability that the number would equal or excede 46.7 that
%one time.

probNotPicked=cdf('norm',46.7,43.1,3.6) %this is the probability that you 
%would get a number less than 46.7 if you drew a single sample from the above
%distribution ONCE


N=100; %N is the number of times a randome value is picked from the 
%distribution during the course of the model run.  I am guessing N= some
%number that depends on the number of itterations, and that N is very very
%big.  To proove the point I'm using 100

probNeverPicked=probNotPicked^N %this is the probability that the number is 
%never picked during N itterations.

probPickedAtLeastOnceAfterNTries=1-probNeverPicked %this is the probability that a 
%number greater than or equal to 46.7 is drawn from the distribution at
%least once over N itterations


%I should point out that I am assuming the model is using the priors in
%some type of monte-carlo esq sence to generate guesses that are then 
%evaluated, if this is not the case, then this
%math doesn't really apply.



